Disclaimer

The following work is preliminary and intended only as tool for eliciting feedback on data, modelling and other aspects of this fishery.

None of these results are final.

These analyses do not necessarily reflect the point of view of NOAA and in no way anticipate NOAA future policy.

Objective

Develop an MSE framework for the Atlantic Dolphinfish (mahi mahi) fishery to test candidate management procedures and inform other managemetn decision making including research prioritization, assessment methodology, specification of fishing regulations and enforcement.

Project details

‘Lead Analyst for the Dolphin Management Strategy Evaluation Project IAW the Tasks included in the Statement of Work’

Term June 2024 - May 2025, June 2025 - May 2026
Funding body U.S. National Oceanic and Atmospheric Administration
Funding stream Sam.gov
Solicitation No. # 1305M324Q0309, NA
Contract No. 1305M324P0270, NA
Project Partners Blue Matter Science Ltd.
Blue Matter Team Tom Carruthers, Adrian Hordyk, Quang Huynh
NOAA Collaborators Cassidy Peterson, Matt Damiano

Progress

The Prerequisite phase has been completed. MSE framework development is current in the Foundation and Initial phases (Figure 1). Rapid progress is expected towards the Revision phase since much of the supporting meetings, research and analyses have been completed.

Figure 1. Progress in the MSE roadmap. For more information about the roadmap and the various steps see the supporting document

Operating model conditioning

A fit to an OM (lowM) (.html)

see example fit

 

Resources

TSD

Appendix A. About Management Strategy Evaluation

MSE Concepts

MSE schematic

## ✔ Searching for objects of class  MP  in package:  MSEtool
## ✔ Searching for objects of class  MP  in package:  SAMtool
## ✔ Searching for objects of class  MP  in package:  DLMtool
##   [1] "curEref"      "FMSYref"      "FMSYref50"    "FMSYref75"    "NFref"       
##   [6] "DDSS_4010"    "DDSS_75MSY"   "DDSS_MSY"     "SCA_4010"     "SCA_75MSY"   
##  [11] "SCA_MSY"      "SP_4010"      "SP_75MSY"     "SP_MSY"       "SSS_4010"    
##  [16] "SSS_75MSY"    "SSS_MSY"      "AvC"          "AvC_MLL"      "BK"          
##  [21] "BK_CC"        "BK_ML"        "CC1"          "CC2"          "CC3"         
##  [26] "CC4"          "CC5"          "CompSRA"      "CompSRA4010"  "CurC"        
##  [31] "curE"         "curE75"       "DAAC"         "DBSRA"        "DBSRA_40"    
##  [36] "DBSRA4010"    "DCAC"         "DCAC_40"      "DCAC_ML"      "DCAC4010"    
##  [41] "DCACs"        "DD"           "DD4010"       "DDe"          "DDe75"       
##  [46] "DDes"         "DepF"         "DTe40"        "DTe50"        "DynF"        
##  [51] "EtargetLopt"  "Fadapt"       "Fdem"         "Fdem_CC"      "Fdem_ML"     
##  [56] "Fratio"       "Fratio_CC"    "Fratio_ML"    "Fratio4010"   "GB_CC"       
##  [61] "GB_slope"     "GB_target"    "Gcontrol"     "HDAAC"        "ICI"         
##  [66] "ICI2"         "Iratio"       "Islope1"      "Islope2"      "Islope3"     
##  [71] "Islope4"      "IT10"         "IT5"          "Itarget1"     "Itarget1_MPA"
##  [76] "Itarget2"     "Itarget3"     "Itarget4"     "ItargetE1"    "ItargetE2"   
##  [81] "ItargetE3"    "ItargetE4"    "ITe10"        "ITe5"         "ITM"         
##  [86] "L95target"    "LBSPR"        "LBSPR_MLL"    "Lratio_BHI"   "Lratio_BHI2" 
##  [91] "Lratio_BHI3"  "LstepCC1"     "LstepCC2"     "LstepCC3"     "LstepCC4"    
##  [96] "LstepCE1"     "LstepCE2"     "Ltarget1"     "Ltarget2"     "Ltarget3"    
## [101] "Ltarget4"     "LtargetE1"    "LtargetE4"    "matlenlim"    "matlenlim2"  
## [106] "MCD"          "MCD4010"      "minlenLopt1"  "MRnoreal"     "MRreal"      
## [111] "Rcontrol"     "Rcontrol2"    "SBT1"         "SBT2"         "slotlim"     
## [116] "SPmod"        "SPMSY"        "SPslope"      "SPSRA"        "SPSRA_ML"    
## [121] "YPR"          "YPR_CC"       "YPR_ML"

Operating models

An operating model is a theoretical description of fishery and population dynamics used for the testing of management strategies that could include, for example, data collection protocols, stock assessment methods, harvest control rules, enforcement policies and reference points. In fisheries, operating models are used in closed-loop simulation to test management procedures (aka. harvest strategy) accounting for feedbacks between the system, data, management procedure and implementation. A management procedure is any codifable rule that calculates management advice from data. Management Strategy Evaluation uses closed-loop simulation of management procedures as a core technical component but is a wider process of stakeholder and manager engagement that identifies system uncertainties, performance metrics, viable management procedures, ultimately aiming to adopt an MP for the provision of management advice for an established time period.

 

Reference Case Operating Models

The reference case operating model is used as the single ‘base’ operating model from which reference set and robustness set operating models are specified. Reference and robustness tests are typically 1-factor departures from the reference case OM, however sometimes reference set OMs are organized in a factorial grid across primary axes of uncertainty.

 

Reference Set Operating Models

Reference set operating models span a plausible range of the core uncertainties for states of nature. These are often the types of alternative parameterizations or assumptions that would be included in a stock assessment sensitivity analysis.

The role of the reference set operating models is to provide the central basis for evaluating the performance of candidate management procedures, for example rejecting badly performing harvest strategies.

 

Robustness Set Operating Models

Robustness set operating models are intended to include additional sources of uncertainty for providing further discrimination among management procedures that perform comparably among reference set operating models.

Robustness operating models often represent system states of nature that are not empirically informed or are hypotheses of a subset of stakeholders.

 

Operating Model Specification

operating models were constructed assuming that discrete populations occur at the resolution of statistical area (management area). Models were conditioned using the Rapid Conditioning Model (RCM) of openMSE (SAMtool package, Huynh et al. 2023) and fitted to historical catches, standardized catch-per-unit-effort, sub area age composition data, a current estimate of absolute biomass and biomass trends within statistical area based on bed-level survey data. Given an assumption of asymptotic fleet selectivity and the availability of the absolute biomass estimate, it was possible to estimate natural mortality rate from an informative prior.

The Reference Case operating model presented here is for statistical area 14 which had numerous age-composition data.

MSE-style closed-loop projections were undertaken for the current harvest rate (the principal management guideline) and current catch levels.

 

Figure 1. Simulated spatial distribution

 

Reference Case Operating Model

 

Table 3c. Green urchin assumptions and to-do list

Assumptions To Do
Stat. Area is the biological unit Make generic management performance outputs
Commercial Catch CV of 5% Add correct coords to OM objects by stat area
Annual age data effective sample size of 40 Make a coastwide OM by aggregating data
5% Selectivity at 100mm, full selectivity at 120mm (all Stat Areas) Test open-closure rules
Informative M prior of 0.05 with CV of 15% Work on calculation of coast-wide LRP
Absolute biomass estimates are only final historical year Test efficacy of small-scale spatial closures
Somatic Growth follows a von-B growth equation Robustness (M, unreported catches, somatic growth, rec strength)
Maturity is from 2003 study

 

Meeting Notes etc.

Software and Code

Rapid Conditioning Model (RCM) (Huynh 2024)

OpenMSE (Hordyk et al. 2024)

Recent Presentations

References

Acknowledgements

Many thanks to